Overview

Dataset statistics

Number of variables20
Number of observations4449
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory2.2 MiB
Average record size in memory524.3 B

Variable types

Numeric8
Categorical12

Warnings

EmployeeCount has constant value "1.0" Constant
JobLevel is highly correlated with MonthlyIncomeHigh correlation
MonthlyIncome is highly correlated with JobLevelHigh correlation
JobLevel is highly correlated with MonthlyIncomeHigh correlation
MonthlyIncome is highly correlated with JobLevelHigh correlation
JobLevel is highly correlated with MonthlyIncomeHigh correlation
MonthlyIncome is highly correlated with JobLevelHigh correlation
JobLevel is highly correlated with MonthlyIncome and 1 other fieldsHigh correlation
Department is highly correlated with JobRole and 1 other fieldsHigh correlation
MonthlyIncome is highly correlated with JobLevel and 1 other fieldsHigh correlation
JobRole is highly correlated with JobLevel and 3 other fieldsHigh correlation
EducationField is highly correlated with Department and 1 other fieldsHigh correlation
JobRole is highly correlated with JobLevel and 2 other fieldsHigh correlation
JobSatisfaction is highly correlated with EmployeeCountHigh correlation
JobInvolvement is highly correlated with EmployeeCountHigh correlation
JobLevel is highly correlated with JobRole and 1 other fieldsHigh correlation
Department is highly correlated with JobRole and 2 other fieldsHigh correlation
BusinessTravel is highly correlated with EmployeeCountHigh correlation
EmployeeCount is highly correlated with JobRole and 10 other fieldsHigh correlation
MaritalStatus is highly correlated with EmployeeCountHigh correlation
EnvironmentSatisfaction is highly correlated with EmployeeCountHigh correlation
Gender is highly correlated with EmployeeCountHigh correlation
Education is highly correlated with EmployeeCountHigh correlation
EducationField is highly correlated with Department and 1 other fieldsHigh correlation
EmployeeNumber has unique values Unique
NumCompaniesWorked has 584 (13.1%) zeros Zeros

Reproduction

Analysis started2021-07-24 07:12:23.240657
Analysis finished2021-07-24 07:13:28.621887
Duration1 minute and 5.38 seconds
Software versionpandas-profiling v3.0.0
Download configurationconfig.json

Variables

EmployeeNumber
Real number (ℝ≥0)

UNIQUE

Distinct4449
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7956.865588
Minimum1
Maximum100146
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size34.9 KiB
2021-07-24T09:13:29.107636image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile340.4
Q11735
median5902
Q37014
95-th percentile7903.6
Maximum100146
Range100145
Interquartile range (IQR)5279

Descriptive statistics

Standard deviation17226.92233
Coefficient of variation (CV)2.165038751
Kurtosis24.06146701
Mean7956.865588
Median Absolute Deviation (MAD)1323
Skewness5.034460925
Sum35400095
Variance296766853.1
MonotonicityNot monotonic
2021-07-24T09:13:30.021630image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20491
 
< 0.1%
75531
 
< 0.1%
55161
 
< 0.1%
70261
 
< 0.1%
75611
 
< 0.1%
55121
 
< 0.1%
75571
 
< 0.1%
55081
 
< 0.1%
55041
 
< 0.1%
75371
 
< 0.1%
Other values (4439)4439
99.8%
ValueCountFrequency (%)
11
< 0.1%
21
< 0.1%
71
< 0.1%
81
< 0.1%
111
< 0.1%
121
< 0.1%
141
< 0.1%
151
< 0.1%
161
< 0.1%
181
< 0.1%
ValueCountFrequency (%)
1001461
< 0.1%
1001451
< 0.1%
1001441
< 0.1%
1001431
< 0.1%
1001421
< 0.1%
1001411
< 0.1%
1001401
< 0.1%
1001391
< 0.1%
1001381
< 0.1%
1001371
< 0.1%

Age
Real number (ℝ≥0)

Distinct179
Distinct (%)4.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean416.1874579
Minimum18
Maximum9890
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size34.9 KiB
2021-07-24T09:13:30.961479image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum18
5-th percentile24
Q131
median36
Q345
95-th percentile3437
Maximum9890
Range9872
Interquartile range (IQR)14

Descriptive statistics

Standard deviation1566.205147
Coefficient of variation (CV)3.763220437
Kurtosis18.97471698
Mean416.1874579
Median Absolute Deviation (MAD)7
Skewness4.403735101
Sum1851618
Variance2452998.562
MonotonicityNot monotonic
2021-07-24T09:13:32.297678image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
34223
 
5.0%
35223
 
5.0%
29194
 
4.4%
36191
 
4.3%
31190
 
4.3%
38177
 
4.0%
32170
 
3.8%
33169
 
3.8%
40167
 
3.8%
30164
 
3.7%
Other values (169)2581
58.0%
ValueCountFrequency (%)
1819
 
0.4%
1924
 
0.5%
2032
 
0.7%
2143
 
1.0%
2253
 
1.2%
2338
 
0.9%
2471
1.6%
2576
1.7%
26109
2.4%
27139
3.1%
ValueCountFrequency (%)
98902
< 0.1%
98663
0.1%
96613
0.1%
96123
0.1%
95813
0.1%
95313
0.1%
95292
< 0.1%
93842
< 0.1%
93303
0.1%
92432
< 0.1%

BusinessTravel
Categorical

HIGH CORRELATION

Distinct3
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size306.2 KiB
Travel_Rarely
3147 
Travel_Frequently
843 
Non-Travel
459 

Length

Max length17
Median length13
Mean length13.44841537
Min length10

Characters and Unicode

Total characters59832
Distinct characters17
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowTravel_Rarely
2nd rowTravel_Rarely
3rd rowTravel_Rarely
4th rowTravel_Rarely
5th rowTravel_Rarely

Common Values

ValueCountFrequency (%)
Travel_Rarely3147
70.7%
Travel_Frequently843
 
18.9%
Non-Travel459
 
10.3%

Length

2021-07-24T09:13:33.778327image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-07-24T09:13:34.250649image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
travel_rarely3147
70.7%
travel_frequently843
 
18.9%
non-travel459
 
10.3%

Most occurring characters

ValueCountFrequency (%)
e9282
15.5%
r8439
14.1%
l8439
14.1%
a7596
12.7%
T4449
7.4%
v4449
7.4%
_3990
6.7%
y3990
6.7%
R3147
 
5.3%
n1302
 
2.2%
Other values (7)4749
7.9%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter46485
77.7%
Uppercase Letter8898
 
14.9%
Connector Punctuation3990
 
6.7%
Dash Punctuation459
 
0.8%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e9282
20.0%
r8439
18.2%
l8439
18.2%
a7596
16.3%
v4449
9.6%
y3990
8.6%
n1302
 
2.8%
q843
 
1.8%
u843
 
1.8%
t843
 
1.8%
Uppercase Letter
ValueCountFrequency (%)
T4449
50.0%
R3147
35.4%
F843
 
9.5%
N459
 
5.2%
Connector Punctuation
ValueCountFrequency (%)
_3990
100.0%
Dash Punctuation
ValueCountFrequency (%)
-459
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin55383
92.6%
Common4449
 
7.4%

Most frequent character per script

Latin
ValueCountFrequency (%)
e9282
16.8%
r8439
15.2%
l8439
15.2%
a7596
13.7%
T4449
8.0%
v4449
8.0%
y3990
7.2%
R3147
 
5.7%
n1302
 
2.4%
F843
 
1.5%
Other values (5)3447
 
6.2%
Common
ValueCountFrequency (%)
_3990
89.7%
-459
 
10.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII59832
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e9282
15.5%
r8439
14.1%
l8439
14.1%
a7596
12.7%
T4449
7.4%
v4449
7.4%
_3990
6.7%
y3990
6.7%
R3147
 
5.3%
n1302
 
2.2%
Other values (7)4749
7.9%

DailyRate
Real number (ℝ≥0)

Distinct886
Distinct (%)19.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean801.0179816
Minimum102
Maximum1499
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size34.9 KiB
2021-07-24T09:13:34.854071image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum102
5-th percentile167
Q1458
median804
Q31162
95-th percentile1424.2
Maximum1499
Range1397
Interquartile range (IQR)704

Descriptive statistics

Standard deviation405.605362
Coefficient of variation (CV)0.506362368
Kurtosis-1.229222404
Mean801.0179816
Median Absolute Deviation (MAD)353
Skewness-0.00151943967
Sum3563729
Variance164515.7097
MonotonicityNot monotonic
2021-07-24T09:13:35.699426image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
69117
 
0.4%
53016
 
0.4%
132916
 
0.4%
114616
 
0.4%
108216
 
0.4%
32916
 
0.4%
43015
 
0.3%
128315
 
0.3%
35014
 
0.3%
115713
 
0.3%
Other values (876)4295
96.5%
ValueCountFrequency (%)
1022
 
< 0.1%
1034
0.1%
1043
0.1%
1052
 
< 0.1%
1062
 
< 0.1%
1072
 
< 0.1%
1094
0.1%
1117
0.2%
1153
0.1%
1167
0.2%
ValueCountFrequency (%)
14993
 
0.1%
14983
 
0.1%
14966
0.1%
149510
0.2%
14923
 
0.1%
149013
0.3%
14883
 
0.1%
14859
0.2%
14823
 
0.1%
14807
0.2%

Department
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct3
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size319.7 KiB
Research & Development
2916 
Sales
1345 
Human Resources
 
188

Length

Max length22
Median length22
Mean length16.56484603
Min length5

Characters and Unicode

Total characters73697
Distinct characters20
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowResearch & Development
2nd rowResearch & Development
3rd rowResearch & Development
4th rowResearch & Development
5th rowResearch & Development

Common Values

ValueCountFrequency (%)
Research & Development2916
65.5%
Sales1345
30.2%
Human Resources188
 
4.2%

Length

2021-07-24T09:13:37.118963image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-07-24T09:13:37.592968image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
2916
27.9%
development2916
27.9%
research2916
27.9%
sales1345
12.8%
resources188
 
1.8%
human188
 
1.8%

Most occurring characters

ValueCountFrequency (%)
e16301
22.1%
6020
 
8.2%
s4637
 
6.3%
a4449
 
6.0%
l4261
 
5.8%
R3104
 
4.2%
r3104
 
4.2%
c3104
 
4.2%
o3104
 
4.2%
m3104
 
4.2%
Other values (10)22509
30.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter57208
77.6%
Uppercase Letter7553
 
10.2%
Space Separator6020
 
8.2%
Other Punctuation2916
 
4.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e16301
28.5%
s4637
 
8.1%
a4449
 
7.8%
l4261
 
7.4%
r3104
 
5.4%
c3104
 
5.4%
o3104
 
5.4%
m3104
 
5.4%
n3104
 
5.4%
h2916
 
5.1%
Other values (4)9124
15.9%
Uppercase Letter
ValueCountFrequency (%)
R3104
41.1%
D2916
38.6%
S1345
17.8%
H188
 
2.5%
Space Separator
ValueCountFrequency (%)
6020
100.0%
Other Punctuation
ValueCountFrequency (%)
&2916
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin64761
87.9%
Common8936
 
12.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
e16301
25.2%
s4637
 
7.2%
a4449
 
6.9%
l4261
 
6.6%
R3104
 
4.8%
r3104
 
4.8%
c3104
 
4.8%
o3104
 
4.8%
m3104
 
4.8%
n3104
 
4.8%
Other values (8)16489
25.5%
Common
ValueCountFrequency (%)
6020
67.4%
&2916
32.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII73697
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e16301
22.1%
6020
 
8.2%
s4637
 
6.3%
a4449
 
6.0%
l4261
 
5.8%
R3104
 
4.2%
r3104
 
4.2%
c3104
 
4.2%
o3104
 
4.2%
m3104
 
4.2%
Other values (10)22509
30.5%

DistanceFromHome
Real number (ℝ≥0)

Distinct158
Distinct (%)3.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean33339.43336
Minimum1
Maximum999590
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size34.9 KiB
2021-07-24T09:13:38.194272image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median8
Q317
95-th percentile253620
Maximum999590
Range999589
Interquartile range (IQR)15

Descriptive statistics

Standard deviation145352.1314
Coefficient of variation (CV)4.359766103
Kurtosis22.76712052
Mean33339.43336
Median Absolute Deviation (MAD)6
Skewness4.765374175
Sum148327139
Variance2.112724211 × 1010
MonotonicityNot monotonic
2021-07-24T09:13:39.114862image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2612
 
13.8%
1582
 
13.1%
10249
 
5.6%
3237
 
5.3%
9233
 
5.2%
7233
 
5.2%
8231
 
5.2%
5182
 
4.1%
4169
 
3.8%
6160
 
3.6%
Other values (148)1561
35.1%
ValueCountFrequency (%)
1582
13.1%
2612
13.8%
3237
 
5.3%
4169
 
3.8%
5182
 
4.1%
6160
 
3.6%
7233
 
5.2%
8231
 
5.2%
9233
 
5.2%
10249
5.6%
ValueCountFrequency (%)
9995903
0.1%
9974222
< 0.1%
9931613
0.1%
9929472
< 0.1%
9754463
0.1%
9717872
< 0.1%
9682032
< 0.1%
9639291
 
< 0.1%
9467863
0.1%
9344483
0.1%

Education
Categorical

HIGH CORRELATION

Distinct5
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size260.8 KiB
3.0
1739 
4.0
1196 
2.0
847 
1.0
521 
5.0
 
146

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters13347
Distinct characters7
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row2.0
3rd row3.0
4th row2.0
5th row2.0

Common Values

ValueCountFrequency (%)
3.01739
39.1%
4.01196
26.9%
2.0847
19.0%
1.0521
 
11.7%
5.0146
 
3.3%

Length

2021-07-24T09:13:40.625340image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-07-24T09:13:41.040309image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
3.01739
39.1%
4.01196
26.9%
2.0847
19.0%
1.0521
 
11.7%
5.0146
 
3.3%

Most occurring characters

ValueCountFrequency (%)
.4449
33.3%
04449
33.3%
31739
 
13.0%
41196
 
9.0%
2847
 
6.3%
1521
 
3.9%
5146
 
1.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number8898
66.7%
Other Punctuation4449
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
04449
50.0%
31739
 
19.5%
41196
 
13.4%
2847
 
9.5%
1521
 
5.9%
5146
 
1.6%
Other Punctuation
ValueCountFrequency (%)
.4449
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common13347
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
.4449
33.3%
04449
33.3%
31739
 
13.0%
41196
 
9.0%
2847
 
6.3%
1521
 
3.9%
5146
 
1.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII13347
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
.4449
33.3%
04449
33.3%
31739
 
13.0%
41196
 
9.0%
2847
 
6.3%
1521
 
3.9%
5146
 
1.1%

EducationField
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct6
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size293.5 KiB
Life Sciences
1823 
Medical
1400 
Marketing
486 
Technical Degree
404 
Other
254 

Length

Max length16
Median length13
Mean length10.52753428
Min length5

Characters and Unicode

Total characters46837
Distinct characters26
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowLife Sciences
2nd rowMedical
3rd rowOther
4th rowLife Sciences
5th rowOther

Common Values

ValueCountFrequency (%)
Life Sciences1823
41.0%
Medical1400
31.5%
Marketing486
 
10.9%
Technical Degree404
 
9.1%
Other254
 
5.7%
Human Resources82
 
1.8%

Length

2021-07-24T09:13:42.292433image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-07-24T09:13:42.776323image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
sciences1823
27.0%
life1823
27.0%
medical1400
20.7%
marketing486
 
7.2%
technical404
 
6.0%
degree404
 
6.0%
other254
 
3.8%
resources82
 
1.2%
human82
 
1.2%

Most occurring characters

ValueCountFrequency (%)
e9389
20.0%
i5936
12.7%
c5936
12.7%
n2795
 
6.0%
a2372
 
5.1%
2309
 
4.9%
s1987
 
4.2%
M1886
 
4.0%
L1823
 
3.9%
f1823
 
3.9%
Other values (16)10581
22.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter37770
80.6%
Uppercase Letter6758
 
14.4%
Space Separator2309
 
4.9%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e9389
24.9%
i5936
15.7%
c5936
15.7%
n2795
 
7.4%
a2372
 
6.3%
s1987
 
5.3%
f1823
 
4.8%
l1804
 
4.8%
d1400
 
3.7%
r1226
 
3.2%
Other values (7)3102
 
8.2%
Uppercase Letter
ValueCountFrequency (%)
M1886
27.9%
L1823
27.0%
S1823
27.0%
T404
 
6.0%
D404
 
6.0%
O254
 
3.8%
H82
 
1.2%
R82
 
1.2%
Space Separator
ValueCountFrequency (%)
2309
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin44528
95.1%
Common2309
 
4.9%

Most frequent character per script

Latin
ValueCountFrequency (%)
e9389
21.1%
i5936
13.3%
c5936
13.3%
n2795
 
6.3%
a2372
 
5.3%
s1987
 
4.5%
M1886
 
4.2%
L1823
 
4.1%
f1823
 
4.1%
S1823
 
4.1%
Other values (15)8758
19.7%
Common
ValueCountFrequency (%)
2309
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII46837
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e9389
20.0%
i5936
12.7%
c5936
12.7%
n2795
 
6.0%
a2372
 
5.1%
2309
 
4.9%
s1987
 
4.2%
M1886
 
4.0%
L1823
 
3.9%
f1823
 
3.9%
Other values (16)10581
22.6%

EmployeeCount
Categorical

CONSTANT
HIGH CORRELATION
REJECTED

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size260.8 KiB
1.0
4449 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters13347
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row1.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.04449
100.0%

Length

2021-07-24T09:13:43.917580image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-07-24T09:13:44.318075image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
1.04449
100.0%

Most occurring characters

ValueCountFrequency (%)
14449
33.3%
.4449
33.3%
04449
33.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number8898
66.7%
Other Punctuation4449
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
14449
50.0%
04449
50.0%
Other Punctuation
ValueCountFrequency (%)
.4449
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common13347
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
14449
33.3%
.4449
33.3%
04449
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII13347
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
14449
33.3%
.4449
33.3%
04449
33.3%

EnvironmentSatisfaction
Categorical

HIGH CORRELATION

Distinct4
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size260.8 KiB
3.0
1404 
4.0
1335 
1.0
874 
2.0
836 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters13347
Distinct characters6
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row4.0
2nd row4.0
3rd row3.0
4th row4.0
5th row3.0

Common Values

ValueCountFrequency (%)
3.01404
31.6%
4.01335
30.0%
1.0874
19.6%
2.0836
18.8%

Length

2021-07-24T09:13:45.519320image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-07-24T09:13:46.001750image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
3.01404
31.6%
4.01335
30.0%
1.0874
19.6%
2.0836
18.8%

Most occurring characters

ValueCountFrequency (%)
.4449
33.3%
04449
33.3%
31404
 
10.5%
41335
 
10.0%
1874
 
6.5%
2836
 
6.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number8898
66.7%
Other Punctuation4449
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
04449
50.0%
31404
 
15.8%
41335
 
15.0%
1874
 
9.8%
2836
 
9.4%
Other Punctuation
ValueCountFrequency (%)
.4449
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common13347
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
.4449
33.3%
04449
33.3%
31404
 
10.5%
41335
 
10.0%
1874
 
6.5%
2836
 
6.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII13347
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
.4449
33.3%
04449
33.3%
31404
 
10.5%
41335
 
10.0%
1874
 
6.5%
2836
 
6.3%

Gender
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size268.6 KiB
Male
2681 
Female
1768 

Length

Max length6
Median length4
Mean length4.794785345
Min length4

Characters and Unicode

Total characters21332
Distinct characters6
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowMale
2nd rowFemale
3rd rowFemale
4th rowFemale
5th rowMale

Common Values

ValueCountFrequency (%)
Male2681
60.3%
Female1768
39.7%

Length

2021-07-24T09:13:47.115177image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-07-24T09:13:47.567372image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
male2681
60.3%
female1768
39.7%

Most occurring characters

ValueCountFrequency (%)
e6217
29.1%
a4449
20.9%
l4449
20.9%
M2681
12.6%
F1768
 
8.3%
m1768
 
8.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter16883
79.1%
Uppercase Letter4449
 
20.9%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e6217
36.8%
a4449
26.4%
l4449
26.4%
m1768
 
10.5%
Uppercase Letter
ValueCountFrequency (%)
M2681
60.3%
F1768
39.7%

Most occurring scripts

ValueCountFrequency (%)
Latin21332
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
e6217
29.1%
a4449
20.9%
l4449
20.9%
M2681
12.6%
F1768
 
8.3%
m1768
 
8.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII21332
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e6217
29.1%
a4449
20.9%
l4449
20.9%
M2681
12.6%
F1768
 
8.3%
m1768
 
8.3%

HourlyRate
Real number (ℝ≥0)

Distinct71
Distinct (%)1.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean65.9770735
Minimum30
Maximum100
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size34.9 KiB
2021-07-24T09:13:48.109698image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum30
5-th percentile33
Q148
median66
Q384
95-th percentile97
Maximum100
Range70
Interquartile range (IQR)36

Descriptive statistics

Standard deviation20.27736883
Coefficient of variation (CV)0.3073396219
Kurtosis-1.188254627
Mean65.9770735
Median Absolute Deviation (MAD)18
Skewness-0.02684638745
Sum293532
Variance411.1716865
MonotonicityNot monotonic
2021-07-24T09:13:49.009381image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
9889
 
2.0%
4886
 
1.9%
6685
 
1.9%
5284
 
1.9%
5783
 
1.9%
5683
 
1.9%
8482
 
1.8%
9681
 
1.8%
7981
 
1.8%
5480
 
1.8%
Other values (61)3615
81.3%
ValueCountFrequency (%)
3057
1.3%
3146
1.0%
3271
1.6%
3354
1.2%
3436
0.8%
3547
1.1%
3655
1.2%
3753
1.2%
3841
0.9%
3954
1.2%
ValueCountFrequency (%)
10059
1.3%
9960
1.3%
9889
2.0%
9763
1.4%
9681
1.8%
9572
1.6%
9470
1.6%
9351
1.1%
9277
1.7%
9151
1.1%

JobInvolvement
Categorical

HIGH CORRELATION

Distinct4
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size260.8 KiB
3.0
2629 
2.0
1119 
4.0
454 
1.0
 
247

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters13347
Distinct characters6
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2.0
2nd row2.0
3rd row2.0
4th row3.0
5th row3.0

Common Values

ValueCountFrequency (%)
3.02629
59.1%
2.01119
25.2%
4.0454
 
10.2%
1.0247
 
5.6%

Length

2021-07-24T09:13:50.448237image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-07-24T09:13:50.858996image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
3.02629
59.1%
2.01119
25.2%
4.0454
 
10.2%
1.0247
 
5.6%

Most occurring characters

ValueCountFrequency (%)
.4449
33.3%
04449
33.3%
32629
19.7%
21119
 
8.4%
4454
 
3.4%
1247
 
1.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number8898
66.7%
Other Punctuation4449
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
04449
50.0%
32629
29.5%
21119
 
12.6%
4454
 
5.1%
1247
 
2.8%
Other Punctuation
ValueCountFrequency (%)
.4449
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common13347
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
.4449
33.3%
04449
33.3%
32629
19.7%
21119
 
8.4%
4454
 
3.4%
1247
 
1.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII13347
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
.4449
33.3%
04449
33.3%
32629
19.7%
21119
 
8.4%
4454
 
3.4%
1247
 
1.9%

JobLevel
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct5
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size260.8 KiB
1.0
1666 
2.0
1606 
3.0
650 
4.0
327 
5.0
200 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters13347
Distinct characters7
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2.0
2nd row3.0
3rd row1.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.01666
37.4%
2.01606
36.1%
3.0650
 
14.6%
4.0327
 
7.3%
5.0200
 
4.5%

Length

2021-07-24T09:13:52.060692image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-07-24T09:13:52.496490image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
1.01666
37.4%
2.01606
36.1%
3.0650
 
14.6%
4.0327
 
7.3%
5.0200
 
4.5%

Most occurring characters

ValueCountFrequency (%)
.4449
33.3%
04449
33.3%
11666
 
12.5%
21606
 
12.0%
3650
 
4.9%
4327
 
2.4%
5200
 
1.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number8898
66.7%
Other Punctuation4449
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
04449
50.0%
11666
 
18.7%
21606
 
18.0%
3650
 
7.3%
4327
 
3.7%
5200
 
2.2%
Other Punctuation
ValueCountFrequency (%)
.4449
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common13347
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
.4449
33.3%
04449
33.3%
11666
 
12.5%
21606
 
12.0%
3650
 
4.9%
4327
 
2.4%
5200
 
1.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII13347
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
.4449
33.3%
04449
33.3%
11666
 
12.5%
21606
 
12.0%
3650
 
4.9%
4327
 
2.4%
5200
 
1.5%

JobRole
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct9
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size326.4 KiB
Sales Executive
983 
Research Scientist
913 
Laboratory Technician
781 
Manufacturing Director
430 
Healthcare Representative
397 
Other values (4)
945 

Length

Max length25
Median length18
Mean length18.09125646
Min length7

Characters and Unicode

Total characters80488
Distinct characters29
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowLaboratory Technician
2nd rowManufacturing Director
3rd rowResearch Scientist
4th rowResearch Scientist
5th rowLaboratory Technician

Common Values

ValueCountFrequency (%)
Sales Executive983
22.1%
Research Scientist913
20.5%
Laboratory Technician781
17.6%
Manufacturing Director430
9.7%
Healthcare Representative397
8.9%
Manager299
 
6.7%
Sales Representative252
 
5.7%
Research Director240
 
5.4%
Human Resources154
 
3.5%

Length

2021-07-24T09:13:54.236712image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-07-24T09:13:54.794465image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
sales1235
14.4%
research1153
13.4%
executive983
11.4%
scientist913
10.6%
laboratory781
9.1%
technician781
9.1%
director670
7.8%
representative649
7.5%
manufacturing430
 
5.0%
healthcare397
 
4.6%
Other values (3)607
7.1%

Most occurring characters

ValueCountFrequency (%)
e11868
14.7%
a7786
 
9.7%
t6385
 
7.9%
c6262
 
7.8%
i6120
 
7.6%
r5984
 
7.4%
n4437
 
5.5%
s4258
 
5.3%
4150
 
5.2%
o2386
 
3.0%
Other values (19)20852
25.9%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter67739
84.2%
Uppercase Letter8599
 
10.7%
Space Separator4150
 
5.2%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e11868
17.5%
a7786
11.5%
t6385
9.4%
c6262
9.2%
i6120
9.0%
r5984
8.8%
n4437
 
6.6%
s4258
 
6.3%
o2386
 
3.5%
h2331
 
3.4%
Other values (10)9922
14.6%
Uppercase Letter
ValueCountFrequency (%)
S2148
25.0%
R1956
22.7%
E983
11.4%
L781
 
9.1%
T781
 
9.1%
M729
 
8.5%
D670
 
7.8%
H551
 
6.4%
Space Separator
ValueCountFrequency (%)
4150
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin76338
94.8%
Common4150
 
5.2%

Most frequent character per script

Latin
ValueCountFrequency (%)
e11868
15.5%
a7786
10.2%
t6385
 
8.4%
c6262
 
8.2%
i6120
 
8.0%
r5984
 
7.8%
n4437
 
5.8%
s4258
 
5.6%
o2386
 
3.1%
h2331
 
3.1%
Other values (18)18521
24.3%
Common
ValueCountFrequency (%)
4150
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII80488
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e11868
14.7%
a7786
 
9.7%
t6385
 
7.9%
c6262
 
7.8%
i6120
 
7.6%
r5984
 
7.4%
n4437
 
5.5%
s4258
 
5.3%
4150
 
5.2%
o2386
 
3.0%
Other values (19)20852
25.9%

JobSatisfaction
Categorical

HIGH CORRELATION

Distinct4
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size260.8 KiB
4.0
1395 
3.0
1324 
1.0
889 
2.0
841 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters13347
Distinct characters6
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row4.0
2nd row3.0
3rd row2.0
4th row4.0
5th row4.0

Common Values

ValueCountFrequency (%)
4.01395
31.4%
3.01324
29.8%
1.0889
20.0%
2.0841
18.9%

Length

2021-07-24T09:13:56.142761image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-07-24T09:13:56.544935image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
4.01395
31.4%
3.01324
29.8%
1.0889
20.0%
2.0841
18.9%

Most occurring characters

ValueCountFrequency (%)
.4449
33.3%
04449
33.3%
41395
 
10.5%
31324
 
9.9%
1889
 
6.7%
2841
 
6.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number8898
66.7%
Other Punctuation4449
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
04449
50.0%
41395
 
15.7%
31324
 
14.9%
1889
 
10.0%
2841
 
9.5%
Other Punctuation
ValueCountFrequency (%)
.4449
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common13347
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
.4449
33.3%
04449
33.3%
41395
 
10.5%
31324
 
9.9%
1889
 
6.7%
2841
 
6.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII13347
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
.4449
33.3%
04449
33.3%
41395
 
10.5%
31324
 
9.9%
1889
 
6.7%
2841
 
6.3%

MaritalStatus
Categorical

HIGH CORRELATION

Distinct3
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size277.7 KiB
Married
2074 
Single
1430 
Divorced
945 

Length

Max length8
Median length7
Mean length6.890986739
Min length6

Characters and Unicode

Total characters30658
Distinct characters14
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowMarried
2nd rowMarried
3rd rowMarried
4th rowSingle
5th rowDivorced

Common Values

ValueCountFrequency (%)
Married2074
46.6%
Single1430
32.1%
Divorced945
21.2%

Length

2021-07-24T09:13:57.693969image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-07-24T09:13:58.184909image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
married2074
46.6%
single1430
32.1%
divorced945
21.2%

Most occurring characters

ValueCountFrequency (%)
r5093
16.6%
i4449
14.5%
e4449
14.5%
d3019
9.8%
M2074
6.8%
a2074
6.8%
S1430
 
4.7%
n1430
 
4.7%
g1430
 
4.7%
l1430
 
4.7%
Other values (4)3780
12.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter26209
85.5%
Uppercase Letter4449
 
14.5%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
r5093
19.4%
i4449
17.0%
e4449
17.0%
d3019
11.5%
a2074
7.9%
n1430
 
5.5%
g1430
 
5.5%
l1430
 
5.5%
v945
 
3.6%
o945
 
3.6%
Uppercase Letter
ValueCountFrequency (%)
M2074
46.6%
S1430
32.1%
D945
21.2%

Most occurring scripts

ValueCountFrequency (%)
Latin30658
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
r5093
16.6%
i4449
14.5%
e4449
14.5%
d3019
9.8%
M2074
6.8%
a2074
6.8%
S1430
 
4.7%
n1430
 
4.7%
g1430
 
4.7%
l1430
 
4.7%
Other values (4)3780
12.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII30658
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
r5093
16.6%
i4449
14.5%
e4449
14.5%
d3019
9.8%
M2074
6.8%
a2074
6.8%
S1430
 
4.7%
n1430
 
4.7%
g1430
 
4.7%
l1430
 
4.7%
Other values (4)3780
12.3%

MonthlyIncome
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct1349
Distinct (%)30.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6468.496067
Minimum1009
Maximum19999
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size34.9 KiB
2021-07-24T09:13:58.788363image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum1009
5-th percentile2096
Q12899
median4876
Q38346
95-th percentile17639
Maximum19999
Range18990
Interquartile range (IQR)5447

Descriptive statistics

Standard deviation4682.905422
Coefficient of variation (CV)0.7239558274
Kurtosis1.020613207
Mean6468.496067
Median Absolute Deviation (MAD)2160
Skewness1.373170909
Sum28778339
Variance21929603.19
MonotonicityNot monotonic
2021-07-24T09:13:59.727908image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
234214
 
0.3%
238012
 
0.3%
261012
 
0.3%
274110
 
0.2%
240410
 
0.2%
63479
 
0.2%
34529
 
0.2%
28868
 
0.2%
173288
 
0.2%
20288
 
0.2%
Other values (1339)4349
97.8%
ValueCountFrequency (%)
10093
0.1%
10512
< 0.1%
10522
< 0.1%
10814
0.1%
10914
0.1%
11023
0.1%
11184
0.1%
11294
0.1%
12002
< 0.1%
12233
0.1%
ValueCountFrequency (%)
199992
< 0.1%
199733
0.1%
199432
< 0.1%
199262
< 0.1%
198593
0.1%
198472
< 0.1%
198454
0.1%
198332
< 0.1%
197404
0.1%
197172
< 0.1%

MonthlyRate
Real number (ℝ≥0)

Distinct1427
Distinct (%)32.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean14276.1969
Minimum2094
Maximum26999
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size34.9 KiB
2021-07-24T09:14:00.586691image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum2094
5-th percentile3339
Q17973
median14218
Q320471
95-th percentile25470
Maximum26999
Range24905
Interquartile range (IQR)12498

Descriptive statistics

Standard deviation7149.079135
Coefficient of variation (CV)0.5007691604
Kurtosis-1.224131562
Mean14276.1969
Median Absolute Deviation (MAD)6253
Skewness0.02242348066
Sum63514800
Variance51109332.48
MonotonicityNot monotonic
2021-07-24T09:14:01.518666image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
422312
 
0.3%
123558
 
0.2%
33398
 
0.2%
95588
 
0.2%
104948
 
0.2%
53558
 
0.2%
91508
 
0.2%
73248
 
0.2%
130088
 
0.2%
68818
 
0.2%
Other values (1417)4365
98.1%
ValueCountFrequency (%)
20943
0.1%
20972
 
< 0.1%
21044
0.1%
21124
0.1%
21224
0.1%
21255
0.1%
21372
 
< 0.1%
22274
0.1%
22432
 
< 0.1%
22532
 
< 0.1%
ValueCountFrequency (%)
269993
0.1%
269972
< 0.1%
269682
< 0.1%
269594
0.1%
269562
< 0.1%
269333
0.1%
269144
0.1%
268974
0.1%
268943
0.1%
268622
< 0.1%

NumCompaniesWorked
Real number (ℝ≥0)

ZEROS

Distinct10
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.681726231
Minimum0
Maximum9
Zeros584
Zeros (%)13.1%
Negative0
Negative (%)0.0%
Memory size34.9 KiB
2021-07-24T09:14:02.254085image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median2
Q34
95-th percentile8
Maximum9
Range9
Interquartile range (IQR)3

Descriptive statistics

Standard deviation2.487076504
Coefficient of variation (CV)0.9274162574
Kurtosis0.04224673701
Mean2.681726231
Median Absolute Deviation (MAD)1
Skewness1.040597771
Sum11931
Variance6.185549537
MonotonicityNot monotonic
2021-07-24T09:14:02.837853image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
11594
35.8%
0584
 
13.1%
3493
 
11.1%
2449
 
10.1%
4403
 
9.1%
7228
 
5.1%
6209
 
4.7%
5190
 
4.3%
9156
 
3.5%
8143
 
3.2%
ValueCountFrequency (%)
0584
 
13.1%
11594
35.8%
2449
 
10.1%
3493
 
11.1%
4403
 
9.1%
5190
 
4.3%
6209
 
4.7%
7228
 
5.1%
8143
 
3.2%
9156
 
3.5%
ValueCountFrequency (%)
9156
 
3.5%
8143
 
3.2%
7228
 
5.1%
6209
 
4.7%
5190
 
4.3%
4403
 
9.1%
3493
 
11.1%
2449
 
10.1%
11594
35.8%
0584
 
13.1%

Interactions

2021-07-24T09:12:39.997425image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-24T09:12:40.700743image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-24T09:12:41.356631image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-24T09:12:42.044923image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-24T09:12:42.719277image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-24T09:12:43.412914image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-24T09:12:44.157344image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-24T09:12:44.908594image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-24T09:12:45.549607image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-24T09:12:46.204248image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-24T09:12:46.852286image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-24T09:12:47.518762image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-24T09:12:48.177587image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-24T09:12:48.874043image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-24T09:12:49.597883image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-24T09:12:50.304917image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-24T09:12:51.258363image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-24T09:12:51.991671image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-24T09:12:52.665759image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-24T09:12:53.342600image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-24T09:12:54.039591image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-24T09:12:54.751175image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-24T09:12:55.457039image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-24T09:12:56.118542image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-24T09:12:56.766515image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-24T09:12:57.441820image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-24T09:12:58.100683image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-24T09:12:58.796757image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-24T09:12:59.494695image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-24T09:13:00.216630image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-24T09:13:00.949397image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-24T09:13:01.635207image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-24T09:13:02.298268image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-24T09:13:03.005604image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-24T09:13:03.716862image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-24T09:13:04.449514image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-24T09:13:05.154902image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-24T09:13:05.984483image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-24T09:13:06.747219image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-24T09:13:07.447300image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-24T09:13:08.155008image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-24T09:13:08.901560image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-24T09:13:09.912428image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-24T09:13:10.657114image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-24T09:13:11.386600image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-24T09:13:12.140813image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-24T09:13:12.889775image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-24T09:13:13.619792image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-24T09:13:14.324457image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-24T09:13:15.013239image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-24T09:13:15.664272image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-24T09:13:16.310896image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-24T09:13:16.945185image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-24T09:13:17.633461image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-24T09:13:18.331588image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-24T09:13:18.946693image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-24T09:13:19.603693image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-24T09:13:20.229791image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-24T09:13:20.861559image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-24T09:13:21.496537image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-24T09:13:22.161420image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-24T09:13:22.830431image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-24T09:13:23.495094image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-24T09:13:24.109239image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Correlations

2021-07-24T09:14:03.564547image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2021-07-24T09:14:04.855318image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2021-07-24T09:14:06.231835image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2021-07-24T09:14:07.582502image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.
2021-07-24T09:14:08.886649image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.

Missing values

2021-07-24T09:13:25.497955image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
A simple visualization of nullity by column.
2021-07-24T09:13:27.811362image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

First rows

EmployeeNumberAgeBusinessTravelDailyRateDepartmentDistanceFromHomeEducationEducationFieldEmployeeCountEnvironmentSatisfactionGenderHourlyRateJobInvolvementJobLevelJobRoleJobSatisfactionMaritalStatusMonthlyIncomeMonthlyRateNumCompaniesWorked
010430.0Travel_Rarely852.0Research & Development1.01.0Life Sciences1.04.0Male55.02.02.0Laboratory Technician4.0Married5126.015998.01.0
1163838.0Travel_Rarely397.0Research & Development2.02.0Medical1.04.0Female54.02.03.0Manufacturing Director3.0Married7756.014199.03.0
216426.0Travel_Rarely841.0Research & Development6.03.0Other1.03.0Female46.02.01.0Research Scientist2.0Married2368.023300.01.0
339528.0Travel_Rarely1117.0Research & Development8.02.0Life Sciences1.04.0Female66.03.01.0Research Scientist4.0Single3310.04488.01.0
45335.0Travel_Rarely464.0Research & Development4.02.0Other1.03.0Male75.03.01.0Laboratory Technician4.0Divorced1951.010910.01.0
5146734.0Travel_Rarely1107.0Human Resources9.04.0Technical Degree1.01.0Female52.03.01.0Human Resources3.0Married2742.03072.01.0
672732.0Travel_Rarely1018.0Research & Development3.02.0Life Sciences1.03.0Female39.03.03.0Research Director4.0Single11159.019373.03.0
735142.0Travel_Rarely269.0Research & Development2.03.0Medical1.04.0Female56.02.01.0Laboratory Technician1.0Divorced2593.08007.00.0
855534.0Travel_Frequently296.0Sales6.02.0Marketing1.04.0Female33.01.01.0Sales Representative3.0Divorced2351.012253.00.0
925340.0Travel_Rarely989.0Research & Development4.01.0Medical1.04.0Female46.03.05.0Manager3.0Married19033.06499.01.0

Last rows

EmployeeNumberAgeBusinessTravelDailyRateDepartmentDistanceFromHomeEducationEducationFieldEmployeeCountEnvironmentSatisfactionGenderHourlyRateJobInvolvementJobLevelJobRoleJobSatisfactionMaritalStatusMonthlyIncomeMonthlyRateNumCompaniesWorked
4439797041.0Travel_Rarely582.0Research & Development28.04.0Life Sciences1.01.0Female60.02.04.0Manufacturing Director2.0Married13570.05640.00.0
4440797142.0Travel_Rarely1396.0Research & Development6.03.0Medical1.03.0Male83.03.03.0Research Director1.0Married13348.014842.09.0
4441797242.0Travel_Rarely1396.0Research & Development6.03.0Medical1.03.0Male83.03.03.0Research Director1.0Married13348.014842.09.0
4442797342.0Travel_Rarely1396.0Research & Development6.03.0Medical1.03.0Male83.03.03.0Research Director1.0Married13348.014842.09.0
444379748823.0Travel_Rarely621.0Research & Development15.03.0Medical1.01.0Female73.03.03.0Healthcare Representative4.0Married7978.014075.01.0
444479758823.0Travel_Rarely621.0Research & Development15.03.0Medical1.01.0Female73.03.03.0Healthcare Representative4.0Married7978.014075.01.0
444579768823.0Travel_Rarely621.0Research & Development15.03.0Medical1.01.0Female73.03.03.0Healthcare Representative4.0Married7978.014075.01.0
4446797744.0Non-Travel381.0Research & Development918785.03.0Medical1.01.0Male49.01.01.0Laboratory Technician3.0Single3708.02104.02.0
4447797844.0Non-Travel381.0Research & Development918785.03.0Medical1.01.0Male49.01.01.0Laboratory Technician3.0Single3708.02104.02.0
4448797944.0Non-Travel381.0Research & Development918785.03.0Medical1.01.0Male49.01.01.0Laboratory Technician3.0Single3708.02104.02.0